Unsupervised Domain Adaptation for Disguised-Gait-Based Person Identification on Micro-Doppler Signatures
Unsupervised Domain Adaptation for Disguised-Gait-Based Person Identification on Micro-Doppler Signatures
复制标题
DOI：
10.1109/tcsvt.2022.3161515
复制
发表时间：
2022-09
影响因子：
8.4
通讯作者：
Yang Yang-Yang;Xiaoyi Yang;T. Sakamoto;F. Fioranelli;Beichen Li;Yue Lang
中科院分区：
文献类型：
--
作者：
Yang Yang-Yang;Xiaoyi Yang;T. Sakamoto;F. Fioranelli;Beichen Li;Yue Lang
近年来，基于步态的人识别在安全系统和公共安全取证等各种应用中获得了极大的兴趣。同时，该任务还面临着步态伪装的挑战。当一个人类受试者改变他或她所穿或携带的东西时，使用步态数据可靠地识别受试者的身份变得具有挑战性。在本文中，我们提出了一种无监督域自适应（UDA）模型，命名为类感知条件下的制导子空间对齐（G-SAC）模型，该模型充分利用步态生物特征中的固有信息，基于伪装的步态数据来识别人体受试者。为了实现这一点，我们使用邻域分量分析（NCA）来创建一个内在特征子空间，从中我们可以获得正常步态和伪装步态之间的相似性。通过提出的自适应类感知对齐约束，可以在该子空间的引导下学习类级判别特征表示。我们在微多普勒雷达数据集上的实验结果证明了我们方法的有效性。与几种最先进的方法的比较结果表明，即使在伪装模式与正常步态明显不同的情况下，我们的工作也为相关问题提供了一个有前途的领域自适应解决方案。此外，我们将该方法扩展到更复杂的多目标域自适应（MTDA）挑战和基于视频的步态识别任务中，结果表明该模型在解决日益困难的问题方面具有很大的潜力。
In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject’s identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named Guided Subspace Alignment under the Class-aware condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-the-art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.